- Title
- Supporting competitive robot game mission planning using machine learning
- Creator
- Strydom, Elton
- Subject
- Machine learning
- Subject
- High performance computing
- Subject
- Robotics
- Subject
- LEGO Mindstorms toys Computer programming
- Date Issued
- 2024-04
- Date
- 2024-04
- Type
- Master's theses
- Type
- text
- Identifier
- http://hdl.handle.net/10948/64841
- Identifier
- vital:73929
- Description
- This dissertation presents a study aimed at supporting the strategic planning and execution of missions in competitive robot games, particularly in the FIRST LEGO® League (FLL), through the use of machine learning techniques. The primary objective is to formulate guidelines for evaluating mission strategies using machine learning techniques within the FLL landscape, thereby supporting participants in the mission strategy design journey within the FLL robot game. The research methodology encompasses a literature review, focusing on the current practices in the FLL mission strategy design process. This is followed by a literature review of machine learning techniques on a broad level pivoting towards evolutionary algorithms. The study then delves into the specifics of genetic algorithms, exploring their suitability and potential advantages for mission strategy evaluation in competitive robotic environments within the FLL robot game. A significant portion of the research involves the development and testing of a prototype system that applies a genetic algorithm to simulate and evaluate different mission strategies, providing a practical tool for FLL teams. During the development of the evaluation prototype, guidelines were formulated aligning with the primary research objective which is to formulate guidelines for evaluating mission strategies in robot games using machine learning techniques. Key findings of this study highlight the effectiveness of genetic algorithms in identifying optimal mission strategies. The prototype demonstrates the feasibility of using machine learning to provide real-time, feedback to participating teams, enabling more informed decision-making in the formulation of mission strategies.
- Description
- Thesis (MIT) -- Faculty of Engineering, the Built Environment, and Technology, School of Information Technology, 2024
- Format
- computer
- Format
- online resource
- Format
- application/pdf
- Format
- 1 online resource (xiii, 108 pages)
- Format
- Publisher
- Nelson Mandela University
- Publisher
- Faculty of Engineering, the Built Environment, and Technology
- Language
- English
- Rights
- Nelson Mandela University
- Rights
- All Rights Reserved
- Rights
- Open Access
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NMU School of Information and Communication Technology
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Strydom, E.pdf | 9 MB | Adobe Acrobat PDF | View Details Download |